mirror of
https://github.com/vale981/ray
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63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
from ray.rllib.models.model import Model
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from ray.rllib.models.tf.misc import normc_initializer
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.deprecation import deprecation_warning
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from ray.rllib.utils.framework import get_activation_fn, try_import_tf
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tf = try_import_tf()
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# Deprecated: see as an alternative models/tf/fcnet_v2.py
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class FullyConnectedNetwork(Model):
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"""Generic fully connected network."""
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@override(Model)
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def _build_layers(self, inputs, num_outputs, options):
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"""Process the flattened inputs.
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Note that dict inputs will be flattened into a vector. To define a
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model that processes the components separately, use _build_layers_v2().
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"""
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# Soft deprecate this class. All Models should use the ModelV2
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# API from here on.
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deprecation_warning(
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"Model->FullyConnectedNetwork",
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"ModelV2->FullyConnectedNetwork",
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error=False)
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hiddens = options.get("fcnet_hiddens")
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activation = get_activation_fn(options.get("fcnet_activation"))
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if len(inputs.shape) > 2:
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inputs = tf.layers.flatten(inputs)
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with tf.name_scope("fc_net"):
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i = 1
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last_layer = inputs
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for size in hiddens:
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# skip final linear layer
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if options.get("no_final_linear") and i == len(hiddens):
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output = tf.layers.dense(
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last_layer,
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num_outputs,
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kernel_initializer=normc_initializer(1.0),
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activation=activation,
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name="fc_out")
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return output, output
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label = "fc{}".format(i)
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last_layer = tf.layers.dense(
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last_layer,
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size,
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kernel_initializer=normc_initializer(1.0),
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activation=activation,
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name=label)
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i += 1
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output = tf.layers.dense(
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last_layer,
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num_outputs,
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kernel_initializer=normc_initializer(0.01),
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activation=None,
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name="fc_out")
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return output, last_layer
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